A Survey on DRL based UAV Communications and Networking: DRL Fundamentals, Applications and Implementations
Wei Zhao, Shaoxin Cui, Wen Qiu, Zhiqiang He, Zhi Liu, Xiao Zheng,, Bomin Mao, Nei Kato

TL;DR
This survey reviews how deep reinforcement learning (DRL) techniques are applied to optimize UAV communication networks, addressing challenges like dynamic environments and large-scale scenarios, and highlights future research directions.
Contribution
It provides a comprehensive overview of DRL fundamentals, applications in UAV networks, and implementation details, emphasizing practical problem-solving in complex environments.
Findings
DRL effectively handles dynamic UAV network challenges
DRL algorithms improve optimization in large-scale scenarios
Future directions include multi-agent DRL applications
Abstract
Unmanned aerial vehicles (UAVs) are playing an increasingly pivotal role in modern communication networks,offering flexibility and enhanced coverage for a variety of applica-tions. However, UAV networks pose significant challenges due to their dynamic and distributed nature, particularly when dealing with tasks such as power allocation, channel assignment, caching,and task offloading. Traditional optimization techniques often struggle to handle the complexity and unpredictability of these environments, leading to suboptimal performance. This survey provides a comprehensive examination of how deep reinforcement learning (DRL) can be applied to solve these mathematical optimization problems in UAV communications and networking.Rather than simply introducing DRL methods, the focus is on demonstrating how these methods can be utilized to solve complex mathematical models of the underlying…
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Taxonomy
TopicsTelecommunications and Broadcasting Technologies · Multimedia Communication and Technology · UAV Applications and Optimization
